We have come a long way. The original group trading strategy anticipated the need to resort to a complicated money management strategy. A simple range trading strategy emerged that made complicating steps unnecessary.
Testing from 2011 showed the best chance of earning a profit came from the following settings on the EURUSD:
- 30 分チャート
- Price exceeds 1.5% or more of the 200 SMA
- Buy/sell at market on an expectation of reverting to the SMA
The tests showed a hypothetical net profit of $1,310 取引 1 信号ごとに標準的なロット. Assuming that you’re comfortable using 10:1 レバレッジ, that makes for an annual return of 13.1%. Higher leverage increases the return at increased risk. Lower leverage decreases the return at decreased risk.
Walk forward results
The walk forward results are profitable! That is a huge relief, especially given the amount of effort put into the research.
The thing that disappointed me the most, しかし, is the huge drop in the number of trades. The original strategy placed 60 trades in a 12 ヶ月の期間. The walk forward test only traded 22 回.
The equity curve makes it obvious that nothing happens for large periods of time. Over six months pass between the first trade on January 19, 2012, and the next trade on June 29.
その後, the pace really picks up. 9 月 2012 showed the most activity with 12 合計取引 – more than half of the year’s activity happened in that individual month.
The gross outcome is a profit of $580. The cost of trading, assuming a 2 ピップのスプレッド, は $440. The final return is $140, or a 1.4% return on 10:1 レバレッジ.
Trading efficiencies of the 2012 walk forward results
The substantial drop in the number of trades relates to something that we already knew. The trading strategy needs volatility in order to find trading opportunities.
Whenever volatility drops, which forex brokers bemoaned at the 外国為替の有力者 conference in London, the number of trading opportunities drops, あまりにも.
The walk forward test indicates that this is a great strategy to keep in my pocket for when volatility picks up later this year. It held up on blind data. Although the higher profit facotr likely results from the small sample size, it feels reassuring when the metrics improve on the blind data.
I allowed myself to cheat slightly where I tested the 1% setting in 2012 代わりに 1.5%. The performance showed a similar drop in the number of trades and in the profit. 重要なこと, しかし, the numbers in that sample set improved.
I like this strategy because it is stable. Changing the setting from 1.3% 宛先 1.5% does not cause a drastic U-turn in the performance. 他の言葉で, it’s steady and predictable.
I would feel far less confident in the outcomes if minor changes in the settings yielded enormously different profits or losses. Violent shifts in outcomes from small changes relate to chaos theory. That’s not a desirable trait in an algorithmic system.
Future improvements to the strategy might relate volatility to the entry setting. When volatility is low, 、 1.5% threshold might lower to 1%. As volatility rises, a band of 2% might yield better outcomes in extreme scenarios. An obvious next step would be to relate the movement of the band to the volatility of the EURUSD itself.
このシリーズは、最終的に収益性の高い取引戦略につながった. 旅を読むしたい場合, 記事を順番に読んでほしい
初期 backtests の厄介な驚き
Nice work! What metric would you use for determining the desired volatility for running this strat?
My initial thoughts are to use VIX as a quick and easy proxy for setting an appropriate volatility band. しかし, there is plenty that can go wrong with that type of setup.
The other solution is to build the volatility distribution – the graph that I created in Excel – internally within the code. The strategy could then use standard deviations to dynamically respond to the environment. It’s the best solution in my head. The main obstacle is deciding to proceed with the work.
Could you not apply the strat to a portfolio of markets (or asset classes)? IOW, there is always the needed volatility to be found somewhere.
思います, that would require testing the strat on different markets and asset classes….明らかに.
That’s the next step in the process. I’m going to start with USD pairs. I like forex because it’s exceptionally rare for currencies to go bust. Stocks, 一方、, get delisted every day.
I came up with an easier way to do the standard deviation thing. More coming soon…